deborah burgess, ministry for the environment andrew ... · (source:newton et al. all sites...

Fuzzy logic mapping of high and low-producing grassland

NZEUC, Auckland, 12-14 August, 2019

Deborah Burgess, Ministry for the Environment

Andrew Manderson, Manaaki Whenua, Landcare Research

• Context – Deb

• What was the problem? – Deb

• The real problem – Andrew

• Solution – Andrew

• Outcome – Deb

Outline

Context• New Zealand’s Greenhouse Gas Inventory

Context• New Zealand’s Greenhouse Gas Inventory

• 5 sectors – emissions and removals

EmissionsRemovals

Context• New Zealand’s Greenhouse Gas Inventory

• 5 sectors – emissions and removals

EmissionsRemovals

Context• New Zealand’s Greenhouse Gas Inventory

• 5 sectors – emissions and removals

EmissionsRemovals

Context• New Zealand’s Greenhouse Gas Inventory

• 5 sectors – emissions and removals

EmissionsRemovals

Context• New Zealand’s Greenhouse Gas Inventory

• 5 sectors – emissions and removals

EmissionsRemovals

Context• New Zealand’s Greenhouse Gas Inventory

• 5 sectors – emissions and removals

EmissionsRemovals

Context• New Zealand’s Greenhouse Gas Inventory

• 5 sectors – emissions and removals

• LULUCF – Land Use, Land-Use Change and Forestry

EmissionsRemovals

Context• New Zealand’s Greenhouse Gas Inventory

• 5 sectors – emissions and removals

• LULUCF – Land Use, Land-Use Change and Forestry

Context• New Zealand’s Greenhouse Gas Inventory

• 5 sectors – emissions and removals

• LULUCF – Land Use, Land-Use Change and Forestry

Context• New Zealand’s Greenhouse Gas Inventory

• 5 sectors – emissions and removals

• LULUCF – Land Use, Land-Use Change and Forestry

Context• New Zealand’s Greenhouse Gas Inventory

• 5 sectors – emissions and removals

• LULUCF – Land Use, Land-Use Change and Forestry

Context• New Zealand’s Greenhouse Gas Inventory

• 5 sectors – emissions and removals

• LULUCF – Land Use, Land-Use Change and Forestry

• Four national land use maps, 12 land use classes, 28 years of change

What was the problem?

What was the problem?• We’ve ignored the grass….

What was the problem?• We’ve ignored the grass….

• Actual change not reflected in maps

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

Grassland area 1990 - 2016 (k ha)

Grassland - high producing Grassland - low producing

What was the problem?• We’ve ignored the grass….

• Actual change not reflected in maps

• Inconsistent with data reported elsewhere

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

Grassland area 1990 - 2016 (k ha)

Grassland - high producing Grassland - low producing

What was the problem?• We’ve ignored the grass….

• Actual change not reflected in maps

• Inconsistent with data reported elsewhere

• High and low-producing grassland hard to map from imagery alone

NZTM 1,815,025 5,425,650

What was the problem?• We’ve ignored the grass….

• Actual change not reflected in maps

• Inconsistent with data reported elsewhere

• High and low-producing grassland hard to map from imagery alone

• There is no other single good data source to use

NZTM 1,815,025 5,425,650

What was required?• A transparent, accurate way to map high

and low-producing grassland that...

What was required?• A transparent, accurate way to map high

and low-producing grassland that...

• can be backcast to the 2008 and 2012 maps as well as mapping grassland at 2016 and…

What was required?• A transparent, accurate way to map high

and low-producing grassland that...

• can be backcast to the 2008 and 2012 maps as well as mapping grassland at 2016 and…

• could also be repeated for future versions of the map

… and could we have fries with that?

… and could we have fries with that?• Would it be possible to make a start on

mapping the type of grassland land use by splitting it into:

• Dairy grazing

… and could we have fries with that?• Would it be possible to make a start on

mapping the type of grassland land use by splitting it into:

• Dairy grazing

• Non-dairy grazing

… and could we have fries with that?• Would it be possible to make a start on

mapping the type of grassland land use by splitting it into:

• Dairy grazing

• Non-dairy grazing

• Un-grazed grassland

The real problem• It shouldn’t be a problem!

• We know the grassland extent

• Only two classes – high & low

• Simple. Model it.

• Lots of problems; the two big ones:

• Limited land management data (spatial). Key production driver.

Modelled annual pasture yield(aggregated daily modelling @100m res)(soil/climate driven model)

The real problem• It shouldn’t be a problem!

• We know the grassland extent

• Only two classes – high & low

• Simple. Model it.

• Lots of problems; the two big ones:

• Limited land management data (spatial). Key production driver.

• Pasture yield varies from year to year

(long-term means or annual means?)

Annual pasture production (kg DM ha-1)

(source:Newton et al. all sites MAF-AgRes data)

1-M

onaB

ush

2-W

into

n3-A

rrow

tow

n4-C

rom

well

5-P

oolb

urn

Dry

6-P

oolb

urn

Irri

7-W

estp

ort

8-M

otu

eka

9-M

anutu

jke

10-W

airakeiF

lats

11-W

airakeiH

ill12-M

aste

rton

13-M

ara

ekakaho

14-D

arg

avi

lle15-H

am

ilton

16-R

angitik

eiF

lockH

s17-M

art

on1

18-M

art

on2

19-T

aie

riP

lain

20-T

aie

riH

ill21-W

inchm

ore

Dry

22-W

inchm

ore

Irri

23-S

outh

Kairapa

24-H

indon

25-R

anui

26-K

ow

whitirangi2

8cut

27-A

haura

28cut

28-W

aim

ate

14cut

29-W

aim

ate

28cut

30-S

tratford

14cut

31-S

tratford

28cut

32-W

aere

ngaokuri14cut

33-W

aere

ngaokuri28cut

34-W

indsorD

ry35-R

uakura

NH

A (

kg

DM

ha

-1)

0

5000

10000

15000

20000

25000

Our solution – fuzzy logic• Fuzzy logic grassland classification

• Funny name but serious method

• Abstract concept

• MBIE funded IDA project

• The key idea…

• Most GIS overlay for classification are Boolean (e.g. 0 or 1)

• Fuzzy logic overlay is based on degrees of truth (DoT)• A condition or statement can be true (value =

1.0), false (value = 0.0), or any of a continuum of values in-between.

Method – full overview

1. Literature review to identify definitions2. Definition deconstruction & reconstruction3. Identify & source relevant GIS data layers

Statement: Grassland is considered ‘high producing’ when… e.g. soil fertility is high… water is non-limiting… stocking rates are high… etc.

Method – full overview

1. Literature review2. Definition deconstruction & reconstruction3. Identify & source relevant GIS data layers

4. Data digging & analysis & modelling5. Transform inputs by relationships (fuz mbrs)

Method – (fuzzy relationships – slope)

1. Pasture production f{slope}2. Analysed NZLRI slope class by average CC3. Modelled feed-demand by CC4. Result roughly sigmoidal5. Refined with published expert knowledge

about slope limitations to pasture production6. A slope of 25 has highest uncertainty = 0.5 DoT6. Extract the function & transform 15m NZ slope to

DoT(12 functions for 12 data layers)

(note: ‘fuzzy value’ = degree of truth DoT)

Method – full overview

Method – fuzzy membership layers

Method – full overview

1. ESRI ArcGIS Fuzzy Overlay tools2. (fuzzy gamma overlay reproduced in Excel for testing)3. Intermediary layers

Method – x3 intermediary DoTs

Method – full overview

1. Greater confidence in 0.6 cf. 0.52. Aggregate to minimise complexity3. DoT result

Result (DoT map)• Intermediary output 1 (DoT map)

• (Darker = higher likelihood of being high producing)

Result (uncertainty)• Uncertainty

• (nearness to the threshold value)

Those fries…• Simple land use classification

• But limited spatial land use datasets

• Commercial datasets –publication restrictions

• Ratings data plus modelled ratings data validated with commercial dataset (RD combo)

• Considerable disagreement between land use datasets (spatial & aspatial – see graph)

Outcome• Credible grassland trend through time-

series

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

Grassland area 1990 - 2016 (kha)

Grassland - high producing Grassland - low producing

Outcome• Credible grassland trend through time-

series

• Credible change from low to high-producing grassland

1990 \ 2008Grassland –high producing

Grassland – low producing

Grassland –high producing 5,707 0

Grassland –low producing 2 7,452

1990 \ 2008 Grassland - high producing

Grassland - low producing

Grassland -high producing 5,641 -Grassland -low producing 983 6,476

Before: Grassland change (kha)

After: Grassland change (kha)

Outcome• Credible grassland trend through time-

series

• Credible change from low to high-producing grassland

• Reasonable dairy area – 1.9 million ha

Further information

MfE Data Service :https://data.mfe.govt.nz/

16 national satellite imagery mosaics:

https://www.mfe.govt.nz/more/data/available-datasets/satellite-data-search

Google “satellite data MfE”